23 citations · 28 across the 5 of their papers we have counts for
5 papers
Automated machine learning for borehole resistivity measurements
M. Shahriari, D. Pardo, S. Kargaran +1
Deep neural networks (DNNs) offer a real-time solution for the inversion of borehole resistivity measurements to approximate forward and inverse operators. It is possible to use ex…
Design of borehole resistivity measurement acquisition systems using deep learning
M. Shahriari, A. Hazra, D. Pardo
Borehole resistivity measurements recorded with logging-while-drilling (LWD) instruments are widely used for characterizing the earth's subsurface properties. They facilitate the e…
Error Control and Loss Functions for the Deep Learning Inversion of Borehole Resistivity Measurements
M. Shahriari, D. Pardo, J. A. Rivera +5
Deep learning (DL) is a numerical method that approximates functions. Recently, its use has become attractive for the simulation and inversion of multiple problems in computational…
Modeling extra-deep electromagnetic logs using a deep neural network
Sergey Alyaev, Mostafa Shahriari, David Pardo +4
Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We present a methodology to construct a deep neural network (DNN) mod…
A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements
M. Shahriari, D. Pardo, A. Picón +3
We use borehole resistivity measurements to map the electrical properties of the subsurface and to increase the productivity of a reservoir. When used for geosteering purposes, it…